{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "47df3dec",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7c0b50c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "da3d06ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置中文字体支持\n",
    "plt.rcParams[\"font.family\"] = [\"SimHei\"]\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e7d254cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成模拟数据\n",
    "np.random.seed(42)  # 设置随机种子以确保结果可重现\n",
    "normal_data = np.random.normal(loc=50, scale=10, size=1000)  # 正态分布数据\n",
    "skewed_data = stats.skewnorm.rvs(a=10, loc=50, scale=10, size=1000)  # 右偏分布数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d6de590f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建画布\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n",
    "\n",
    "# 绘制正态分布直方图\n",
    "sns.histplot(normal_data, bins=30, kde=False, ax=axes[0], color='skyblue')\n",
    "axes[0].set_title('正态分布直方图')\n",
    "axes[0].set_xlabel('数值')\n",
    "axes[0].set_ylabel('频数')\n",
    "\n",
    "# 添加统计信息\n",
    "mean_val = np.mean(normal_data)\n",
    "median_val = np.median(normal_data)\n",
    "axes[0].axvline(mean_val, color='red', linestyle='dashed', linewidth=2, label=f'均值: {mean_val:.2f}')\n",
    "axes[0].axvline(median_val, color='green', linestyle='dashed', linewidth=2, label=f'中位数: {median_val:.2f}')\n",
    "axes[0].legend()\n",
    "\n",
    "# 绘制右偏分布直方图\n",
    "sns.histplot(skewed_data, bins=30, kde=False, ax=axes[1], color='salmon')\n",
    "axes[1].set_title('右偏分布直方图')\n",
    "axes[1].set_xlabel('数值')\n",
    "axes[1].set_ylabel('频数')\n",
    "\n",
    "# 添加统计信息\n",
    "mean_val = np.mean(skewed_data)\n",
    "median_val = np.median(skewed_data)\n",
    "axes[1].axvline(mean_val, color='red', linestyle='dashed', linewidth=2, label=f'均值: {mean_val:.2f}')\n",
    "axes[1].axvline(median_val, color='green', linestyle='dashed', linewidth=2, label=f'中位数: {median_val:.2f}')\n",
    "axes[1].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "my_env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
